We are using machine learning to control situations where there are a lot of variables. Data democratization means everyone has access to these data and tools. There are a ton of great tools out there that help folks who maybe aren't data scientists, but are data science-y and make better decisions at work. The growth of artificial intelligence and machine learning over the last few years is unmistakable. Agencies have realized the potential and real benefits of using the advanced technologies to improve decision making, analyze large databases and address mission challenges.

Dr. David Bray is the Inaugural Director of the new global GeoTech Center & Commission of the Atlantic Council, a nonprofit for international political, business, and intellectual leaders founded in 1961. Headquartered in Washington, DC, the Council offers programs related to international security and global economic prosperity. In previous leadership roles, Bray led the technology aspects of the Centers for Disease Control's bioterrorism preparedness program in response to 9/11, the outbreak response to the West Nile virus, SARS, monkey pox and other emergencies. He also spent time on the ground in Afghanistan in 2009 as a senior advisor to both military and humanitarian assistance efforts, serving as the non-partisan Executive Director for a bipartisan National Commission on R&D, and providing leadership as a non-partisan federal agency Senior Executive focused on digital modernization. He also is a Young Global Leader for 2017-2021 of the World Economic Forum. Bray is a member of multiple Boards of Directors and has worked with the U.S. Special Operations Command on counter-misinformation efforts. He was invited to give the 2019 UN Charter Keynote on the future of AI & IoT governance. His academic background includes a PhD from Emory University; he also has held affiliations with MIT, Harvard, and the University of Oxford. He recently took a few moments to speak to AI Trends Editor John P. Desmond about current events, including the geopolitics of the COVID-19 pandemic. AI Trends: Thank you David for talking to AI Trends today.

Over the last several years, the use of artificial intelligence (AI) and machine learning (ML) has maintained consistent growth among businesses. During our 2017 survey of IT decision makers in the United States and Japan, we discovered that approximately 74% of businesses in both regions were already using some form of AI or ML to protect their organizations from cyber threats. When we checked in with both regions at the end of 2018, 73% of respondents we surveyed reported they planned to use even more AI/ML tools in the following year. For this report, we surveyed 800 IT professionals with cybersecurity decision-making power across the US, UK, Japan, and Australia/New Zealand regions at the end of 2019, and discovered that 96% of respondents now use AI/ML tools in their cybersecurity programs. Despite the increase in adoption rates for these technologies, more than half of IT decision makers admitted they do not fully understand the benefits of these tools.

Alana'CityStyleBot' is giving high street and independent fashion retailers an alternative virtual shop front to serve customers post COVID-19. Launched in February 2020, Cork Start Up Alana is an innovative Fashion and Beauty platform for consumers to purchase curated fashion looks and beauty products. Alana is powered by Artificial Intelligence meaning that it learns to recommend styles/brands that will suit each customer's unique taste. Alana suggests clothes from high street retailers and independent boutiques with a same day delivery service making the whole highstreet a virtual shopping center – one checkout – one delivery charge of €3.99. Alana helps retailers compete with major brands who have an established ecommerce foothold. According to ACI Worldwide there is a 74% growth in the average transaction volumes due to a dramatic rise in online retail this March in comparison to March 2019.

CloudWalk has set up a facility in an AI industry park in Zhangjiang, displaying latest technologies and services including smart city projects. Chinese artificial intelligence firm CloudWalk Technology has raised 1.8 billion yuan (US$257 million) in its latest round of financing, the company said on Thursday. Investors in the new round include China Internet Industry Fund, Shanghai-based Guosheng Group, Guangzhou-based Nansha Financial Holdings and Industrial and Commercial Bank of China (ICBC), the country's biggest bank. The latest investment points to rebounded market confidence and marks CloudWalk's next step toward an initial public offering, the company said in a statement. CloudWork is among China's "Four AI Dragons," along with Megvii, SenseTime and Yitu, each of which is valued at more than US$1 billion.

Jensen Huang's much-anticipated keynote speech today, postponed from Nvidia's GPU Technology Conference (GTC) in March, will unveil the company's eighth-generation GPU architecture. Emerging three years after the debut of the previous generation Volta architecture, Ampere is said to be the biggest generational leap in the company's history. Ampere is built to accelerate both AI training and inference, as well as data analytics, scientific computing and cloud graphics. The first chip built on Ampere, the A100, has some pretty impressive vital statistics. Nvidia claims the A100 has 20x the performance of the equivalent Volta device for both AI training (single precision, 32-bit floating point numbers) and AI inference (8-bit integer numbers).

Artificial intelligence is a scientific field that is responsible for finding solutions to complex problems that humans do not have. Machine learning could be used to bypass and dismantle cyber-security systems faster than most prevention and detection tools can keep up. AI will exacerbate existing threats and create new ones, but its speed could prove a great boon for cybercriminals, as it is much more effective at fighting them than human experts. The algorithm is attempted to model a decision mechanism that resembles real human decision mechanisms but is modeled by algorithms. In the context of cybersecurity, artificial intelligence (AI) tries to defend the system by weighing patterns of behavior that indicate a threat against predictive logic.

ABSTRACT We describe an Object Oriented Model for building Expert Systems. This model and the detection of similarities allow to implement reasoning modes as induction, deduction and simulation. We specially focus on similarity and its use in induction. We propose original algorithms which deal with total and partial structural similitude of objects to facilitate knowledge acquisition. Keywords: Knowledge acquisition, object oriented model, structural similarity, induction. Colloc, J. & Boulanger, D. Automatic knowledge acquisition for object oriented expert systems AVIGNON'93, 13th International Conference Artificial Intelligence, Expert Systems, Natural Language, 1993, 99-108 (Preprint version) 1. INTRODUCTION This paper proposes an object oriented model for building expert systems. While this model enhances the knowledge modularity, it supports some other reasoning modes than traditional deduction. First, we present the characteristics of our object oriented model (COLL 89), then we highlight the features used to implement reasoning and allow knowledge acquisition.

Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.

We present a new approach to solve the sparse approximation or best subset selection problem, namely find a $k$-sparse vector ${\bf x}\in\mathbb{R}^d$ that minimizes the $\ell_2$ residual $\lVert A{\bf x}-{\bf y} \rVert_2$. We consider a regularized approach, whereby this residual is penalized by the non-convex $\textit{trimmed lasso}$, defined as the $\ell_1$-norm of ${\bf x}$ excluding its $k$ largest-magnitude entries. We prove that the trimmed lasso has several appealing theoretical properties, and in particular derive sparse recovery guarantees assuming successful optimization of the penalized objective. Next, we show empirically that directly optimizing this objective can be quite challenging. Instead, we propose a surrogate for the trimmed lasso, called the $\textit{generalized soft-min}$. This penalty smoothly interpolates between the classical lasso and the trimmed lasso, while taking into account all possible $k$-sparse patterns. The generalized soft-min penalty involves summation over $\binom{d}{k}$ terms, yet we derive a polynomial-time algorithm to compute it. This, in turn, yields a practical method for the original sparse approximation problem. Via simulations, we demonstrate its competitive performance compared to current state of the art.